The FullStory digital intelligence platform lets you replay customers' website journeys to solve problems, find answers, and optimize customers' experience. It features funnel analytics, click maps, and robust search and segmentation.

When it was released in 2013, Amazon Redshift was the first cloud data warehouse. It uses defined schemas, columnar data storage, and massively parallel processing (MPP) architecture to provide a base for analytics reporting.

You can use the FullStory API to get a list of sessions for a particular user. For example, to get information based on a user's email address, you could GET https://www.fullstory.com/api/v1/sessions?email=john@example.com.

Here's an example of the kind of response you might see with a query like the one above.

[{
 "UserId": 1234567890,
 "SessionId": 1234567890,
 "CreatedTime": 1411492739,
 "FsUrl": "https://www.fullstory.com/ui/ORG_ID/discover/session/1234567890:1234567890"
}]

Once you have identified all of the columns you will want to insert, you can use the CREATE TABLE statement in Redshift to create a table that can receive all of this data.

With a table built, it may seem like the easiest way to migrate your data (especially if there isn't much of it) is to build INSERT statements to add data to your Redshift table row by row. If you have any experience with SQL, this will be your gut reaction. But beware! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you would be better off loading the data into Amazon S3 and then using the COPY command to load it into Redshift.

Now what? You've built a script that pulls data from FullStory and loads it into your data warehouse, but what happens tomorrow when you have new transactions?

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, many of FullStory's API results include fields like CreatedTime that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

Redshift is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure SQL Data Warehouse, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To BigQuery, To Postgres, To Snowflake, To Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.